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When used in cell therapy and regenerative medicine strategies, stem cells have potential to treat many previously incurable diseases. However, current application methods using stem cells are underdeveloped, as these cells are used directly regardless of their culture medium and subgroup. For example, when using mesenchymal stem cells (MSCs) in cell therapy, researchers do not consider their source and culture method nor their application angle and function (soft tissue regeneration, hard tissue regeneration, suppression of immune function, or promotion of immune function). By combining machine learning methods (such as deep learning) with data sets obtained through single-cell RNA sequencing (scRNA-seq) technology, we can discover the hidden structure of these cells, predict their effects more accurately, and effectively use subpopulations with differentiation potential for stem cell therapy. scRNA-seq technology has changed the study of transcription, because it can express single-cell genes with single-cell anatomical resolution. However, this powerful technology is sensitive to biological and technical noise. The subsequent data analysis can be computationally difficult for a variety of reasons, such as denoising single cell data, reducing dimensionality, imputing missing values, and accounting for the zero-inflated nature. In this review, we discussed how deep learning methods combined with scRNA-seq data for research, how to interpret scRNA-seq data in more depth, improve the follow-up analysis of stem cells, identify potential subgroups, and promote the implementation of cell therapy and regenerative medicine measures.

While the capacity to regenerate tissues or limbs is limited in mammals, including humans, axolotls are able to regrow entire limbs and major organs after incurring a wound. The wound blastema has been extensively studied in limb regeneration. However, due to the inadequate characterization of ECM and cell subpopulations involved in the regeneration process, the discovery of the key drivers for human limb regeneration remains unknown. In this study, we applied large-scale single-cell RNA sequencing to classify cells throughout the adult axolotl limb regeneration process, uncovering a novel regeneration-specific mitochondria-related cluster supporting regeneration through energy providing and the ECM secretion (COL2+) cluster contributing to regeneration through cell-cell interactions signals. We also discovered the dedifferentiation and re-differentiation of the COL1+/COL2+ cellular subpopulation and exposed a COL2-mitochondria subcluster supporting the musculoskeletal system regeneration. On the basis of these findings, we reconstructed the dynamic single-cell transcriptome of adult axolotl limb regenerative process, and identified the novel regenerative mitochondria-related musculoskeletal populations, which yielded deeper insights into the crucial interactions between cell clusters within the regenerative microenvironment.

Tendon maturation lays the foundation for postnatal tendon development, its proper mechanical function, and regeneration, but the critical cell populations and the entangled mechanisms remain poorly understood. Here, by integrating the structural, mechanical, and molecular properties, we show that post-natal days 7-14 are the crucial transitional stage for mouse tendon maturation. We decode the cellular and molecular regulatory networks at the single-cell level. We find that a nerve growth factor (NGF)-secreting Cd9

Although being of utmost importance for human health and mobility, stem cell identity and hierarchical organization of musculoskeletal progenitors remain largely unexplored. Here, cells from E10.5, E12.5, and E15.5 murine limbs are analyzed by high throughput single‐cell RNA sequencing to illustrate the cellular architecture during limb development. Single‐cell transcriptional profiling demonstrates the identity and differentiation architecture of musculoskeletal stem cells (MSSC), soft and hard tissue progenitors through expression pattern of musculoskeletal markers (scleraxis [Scx], Hoxd13, Sox9, and Col1a1). This is confirmed by genetic in vivo lineage tracing. Moreover, single‐cell analyses of Scx knockout mice tissues illustrates that Scx regulates MSSC self‐renewal and proliferation potential. A high‐throughput and low‐cost multi‐tissues RNA sequencing strategy further provides evidence that musculoskeletal system tissues, including muscle, bone, meniscus, and cartilage, are all abnormally developed in Scx knockout mice. These results establish the presence of an indispensable limb Scx+Hoxd13+ MSSC population and their differentiation into soft tissue progenitors (Scx+Col1a1+) and hard tissue progenitors (Scx+Sox9+). Collectively, this study paves the way for systematically decoding the complex molecular mechanisms and cellular programs of musculoskeletal tissues morphogenesis in limb development and regeneration., This study presents a comprehensive genomics resource that reveals the transcriptome dynamics of the developing limb. Single‐cell transcriptional profiling with lineage tracing data demonstrates the identity and differentiation trajectory of musculoskeletal stem cells into soft and hard tissues. These results emphasize an indispensable role of limb Scx+ musculoskeletal stem cell population and provide additional perspective on musculoskeletal biology.

In article number 2000938, Zi Yin, Junxin Lin, and co‐workers identify a novel musculoskeletal stem cell population by single‐cell transcriptional profiling with lineage tracing data during limb development. The differentiation potential and trajectory of musculoskeletal stem cells showed they could contribute to soft and hard connective tissues, as well as muscular tissue. These results suggest an indispensable role of limb Scx+ musculoskeletal stem cell population in musculoskeletal system morphogenesis. [Image: see text]